Market Data Engineer.

Millennium Management
Greater London
10 months ago
Applications closed

Related Jobs

View all jobs

Lead Data Engineer - Nottingham City

Senior Data Engineer

Lead Data Engineer

Junior Data Engineer

Data Engineer

Data Engineer / Technical Lead AWS

Market Data Engineer

The SPEED Market Data team seeks a multi skilled engineer who is excited to support, monitor, architect, and implement low latency C++ systems that are robust, resilient, accurate, stable, and exceedingly fast. By building and maintaining this high-performance infrastructure, this engineer will help position MLP as a leader in the field of quantitative trading. The successful candidate will work alongside other exceptional engineers and strategists to solve business critical that are vital to our systematic trading business.

We are looking for a strong candidate with financial markets technology experience and realtime market data expertise to build and support globally our realtime (both low latency and non-latency sensitive) market data plant. The ideal candidate must be comfortable with monitoring, support, development, and client management duties with goals of ensuring stability of the existing environment whilst also designing and implementing platform improvements.

Principal Responsibilities

Support and management of both enterprise and latency sensitive realtime market data environments, including management of internal, vendor, and exchange-initiated changes Liaison with users of the market data environment, including Portfolio Managers, trading desks, and core technology teams Contributing towards the team’s technical direction by driving new initiatives Collaborating with hardware and software developers across divisions to build realtime market data processing and distribution systems Optimizing this platform by using network and systems programming, as well as other advanced techniques to minimize latency Design and engineering of components to automate support and management capabilities for the market data platform, including monitoring, realtime and historic metrics collection/visualization, and administrative functions including self-service user facing tools Enhancement of processes and workflows related to operation of the market data platform, such as release deployment, incident management and remediation, exchange notification handling, defining and enforcing SLAs

Qualifications/Skills Required

Technical experience supporting market data environments within a global organization, including both internally developed feed handlers and distribution infrastructure Strong understanding of market data concepts and functionality, such as data models (fields/messages), protocols (e.g. snapshot + delta), order book representations (e.g. L1/L2/L3), recovery and reliability Technical background in application development on complex market data systems (i.e. – Bloomberg, Thompson Reuters, etc) Hands on Site Reliability Engineering or operational skills, including system administration, automation, measurement, release / deployment management Experience with monitoring, metrics and command/control functionality for distributed market data platforms; ability to evaluate existing solutions and drive enhancements through coordination of development and operations teams A degree in computer science or a related field with a strong background in object-oriented programming or scripting languages Good understanding of Linux system internals and networking Deep understanding of CPU architecture and the ability to leverage CPU capabilities Able to prioritize in a fast moving, high pressure, constantly changing environment with a good sense of urgency and ownership Effective communication and relationship management skills (client and vendor): The candidate will be expected to work closely with business and technology users to understand their current and future needs Demonstrate thoroughness and strong ownership of work through a detail-oriented approach

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How Many Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

What Hiring Managers Look for First in Data Science Job Applications (UK Guide)

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

The Skills Gap in Data Science Jobs: What Universities Aren’t Teaching

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.